Dear R-helpers,
Using the HBR (high breakdown rank-based) robust estimator and the hbrfit
function, I get an error saying Error in UseMethod("predict") for
hbrfit. How can I solve the problem ? Many thanks for your help.
# # # # # # # # # # # # # # # # # # # # # # # #
install.packages( "robustbase",dependencies=TRUE )
install.packages( "boot",dependencies=TRUE )
install.packages( "MASS",dependencies=TRUE ?)
install.packages( "quantreg",dependencies=TRUE ?)
install.packages( "RobPer",dependencies=TRUE ?)
install_github("kloke/hbrfit")
install.packages('http://www.stat.wmich.edu/mckean/Stat666/Pkgs/npsmReg2_0.1.1.tar.gz')
install.packages( "RobStatTM",dependencies=TRUE )
library(boot)
library(robustbase)
library(MASS)
library(quantreg)
library(RobPer)
library(hbrfit)
library(RobStatTM)
n<-200
b<-runif(n, 0, 5)
z <- rnorm(n, 2, 3)
a <- runif(n, 0, 5)
y_model<- 0.1*b - 0.5 * z - a + 10
y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) )
df<-data.frame(b,z,a,y_obs)
?# function to obtain MSE
?MSE <- function(data, indices, formula){
? ? d <- data[indices, ] # allows boot to select sample
? ? fit <- hbrfit(formula, data = d)
? ? ypred <- predict(fit)
? ?mean((d[["y_obs"]]-ypred)^2)
?}
?# Make the results reproducible
?set.seed(1234)
?
# bootstrapping with 600 replications
results <- boot(data = df, statistic = MSE,
R = 600, formula = y_obs ~ b+z+a)
str(results)
boot.ci(results, type="bca"?)
# # # # # # # # # # # # # # # # # # # # # # # # #
Don't use packages that haven't been maintained. Or correspond with the author. Or, fix them yourself. On March 21, 2020 4:09:13 PM PDT, varin sacha via R-help <r-help at r-project.org> wrote:>Dear R-helpers, > >Using the HBR (high breakdown rank-based) robust estimator and the >hbrfit function, I get an error saying Error in UseMethod("predict") >for hbrfit. How can I solve the problem ? Many thanks for your help. > ># # # # # # # # # # # # # # # # # # # # # # # # >install.packages( "robustbase",dependencies=TRUE ) >install.packages( "boot",dependencies=TRUE ) >install.packages( "MASS",dependencies=TRUE ?) >install.packages( "quantreg",dependencies=TRUE ?) >install.packages( "RobPer",dependencies=TRUE ?) >install_github("kloke/hbrfit") >install.packages('http://www.stat.wmich.edu/mckean/Stat666/Pkgs/npsmReg2_0.1.1.tar.gz') > >install.packages( "RobStatTM",dependencies=TRUE ) > >library(boot) >library(robustbase) >library(MASS) >library(quantreg) >library(RobPer) >library(hbrfit) >library(RobStatTM) > >n<-200 >b<-runif(n, 0, 5) >z <- rnorm(n, 2, 3) >a <- runif(n, 0, 5) > >y_model<- 0.1*b - 0.5 * z - a + 10 >y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) ) >df<-data.frame(b,z,a,y_obs) > >?# function to obtain MSE >?MSE <- function(data, indices, formula){ >? ? d <- data[indices, ] # allows boot to select sample >? ? fit <- hbrfit(formula, data = d) >? ? ypred <- predict(fit) > >? ?mean((d[["y_obs"]]-ypred)^2) >?} > >?# Make the results reproducible >?set.seed(1234) >? ># bootstrapping with 600 replications >results <- boot(data = df, statistic = MSE, >R = 600, formula = y_obs ~ b+z+a) >str(results) > >boot.ci(results, type="bca"?) ># # # # # # # # # # # # # # # # # # # # # # # # # > >______________________________________________ >R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code.-- Sent from my phone. Please excuse my brevity.
On 3/21/20 4:09 PM, varin sacha via R-help wrote:> Dear R-helpers, > > Using the HBR (high breakdown rank-based) robust estimator and the hbrfit function, I get an error saying Error in UseMethod("predict") for hbrfit. How can I solve the problem ? Many thanks for your help.What makes you think there is a predict method for objects returned by hbrfit? (I'm also puzzled how one would construct such a prediction. How would one construct an estimate of something based on a weighted Wilcoxon scale when the weighting depends on the original data? What weights would apply to the new data?) I do note that there are residuals in the object returned from https://github.com/kloke/hbrfit/blob/master/R/hbrfit.R Perhaps you can do something with that? -- David> > # # # # # # # # # # # # # # # # # # # # # # # # > install.packages( "robustbase",dependencies=TRUE ) > install.packages( "boot",dependencies=TRUE ) > install.packages( "MASS",dependencies=TRUE ?) > install.packages( "quantreg",dependencies=TRUE ?) > install.packages( "RobPer",dependencies=TRUE ?) > install_github("kloke/hbrfit") > install.packages('http://www.stat.wmich.edu/mckean/Stat666/Pkgs/npsmReg2_0.1.1.tar.gz') > install.packages( "RobStatTM",dependencies=TRUE ) > > library(boot) > library(robustbase) > library(MASS) > library(quantreg) > library(RobPer) > library(hbrfit) > library(RobStatTM) > > n<-200 > b<-runif(n, 0, 5) > z <- rnorm(n, 2, 3) > a <- runif(n, 0, 5) > > y_model<- 0.1*b - 0.5 * z - a + 10 > y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) ) > df<-data.frame(b,z,a,y_obs) > > ?# function to obtain MSE > ?MSE <- function(data, indices, formula){ > ? ? d <- data[indices, ] # allows boot to select sample > ? ? fit <- hbrfit(formula, data = d) > ? ? ypred <- predict(fit) > > ? ?mean((d[["y_obs"]]-ypred)^2) > ?} > > ?# Make the results reproducible > ?set.seed(1234) > > # bootstrapping with 600 replications > results <- boot(data = df, statistic = MSE, > R = 600, formula = y_obs ~ b+z+a) > str(results) > > boot.ci(results, type="bca"?) > # # # # # # # # # # # # # # # # # # # # # # # # # > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
Hi Jeff,
Hi David,
Thanks for your responses. As the predict function does not work with hbrfit, I
have tried? something without the predict function. There is an error message
(Error in .subset2(x, i, exact = exact)) unclear to me. Many thanks for your
help.
# # # # # # # # # # # # # # # # # # # # # # # #
install.packages( "boot",dependencies=TRUE )
install_github("kloke/hbrfit")
install.packages('http://www.stat.wmich.edu/mckean/Stat666/Pkgs/npsmReg2_0.1.1.tar.gz')
install.packages( "robustbase",dependencies=TRUE )
install.packages( "quantreg",dependencies=TRUE ?)
library(robustbase)
library(quantreg)
library(boot)
library(hbrfit)
n<-50
b<-runif(n, 0, 5)
z <- rnorm(n, 2, 3)
a <- runif(n, 0, 5)
?
y_model<- 0.1*b - 0.5 * z - a + 10
y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) )
df<-data.frame(b,z,a,y_obs)
HBR<-hbrfit(y_obs ~ b+z+a)
# function to obtain MSE
MSE <- function(data, indices, formula) {
? ? d <- data[indices, ] # allows boot to select sample
? ? fit <- hbrfit(formula, data = d)
? ? ypred <- y_model
? ?mean((d[[HBR$fitted.values]]-ypred)^2)
?}
# Make the results reproducible
?set.seed(1234)
?
# bootstrapping with 60 replications
?results <- boot(data = df, statistic = MSE,
? ??????????????? R = 60, formula = y_obs ~ b+z+a)
str(results)
boot.ci(results, type="bca"?)
# # # # # # # # # # # # # # # # # # # # # # # #
?
Le dimanche 22 mars 2020 ? 01:42:49 UTC+1, David Winsemius <dwinsemius at
comcast.net> a ?crit :
On 3/21/20 4:09 PM, varin sacha via R-help wrote:> Dear R-helpers,
>
> Using the HBR (high breakdown rank-based) robust estimator and the hbrfit
function, I get an error saying Error in UseMethod("predict") for
hbrfit. How can I solve the problem ? Many thanks for your help.
What makes you think there is a predict method for objects returned by
hbrfit?
(I'm also puzzled how one would construct such a prediction. How would
one construct an estimate of something based on a weighted Wilcoxon
scale when the weighting depends on the original data? What weights
would apply to the new data?)
I do note that there are residuals in the object returned from
https://github.com/kloke/hbrfit/blob/master/R/hbrfit.R
Perhaps you can do something with that?
--
David
>
> # # # # # # # # # # # # # # # # # # # # # # # #
> install.packages( "robustbase",dependencies=TRUE )
> install.packages( "boot",dependencies=TRUE )
> install.packages( "MASS",dependencies=TRUE ?)
> install.packages( "quantreg",dependencies=TRUE ?)
> install.packages( "RobPer",dependencies=TRUE ?)
> install_github("kloke/hbrfit")
>
install.packages('http://www.stat.wmich.edu/mckean/Stat666/Pkgs/npsmReg2_0.1.1.tar.gz')
> install.packages( "RobStatTM",dependencies=TRUE )
>
> library(boot)
> library(robustbase)
> library(MASS)
> library(quantreg)
> library(RobPer)
> library(hbrfit)
> library(RobStatTM)
>
> n<-200
> b<-runif(n, 0, 5)
> z <- rnorm(n, 2, 3)
> a <- runif(n, 0, 5)
>
> y_model<- 0.1*b - 0.5 * z - a + 10
> y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) )
> df<-data.frame(b,z,a,y_obs)
>
>? ?# function to obtain MSE
>? ?MSE <- function(data, indices, formula){
>? ? ? d <- data[indices, ] # allows boot to select sample
>? ? ? fit <- hbrfit(formula, data = d)
>? ? ? ypred <- predict(fit)
>
>? ? ?mean((d[["y_obs"]]-ypred)^2)
>? ?}
>
>? ?# Make the results reproducible
>? ?set.seed(1234)
>?
> # bootstrapping with 600 replications
> results <- boot(data = df, statistic = MSE,
> R = 600, formula = y_obs ~ b+z+a)
> str(results)
>
> boot.ci(results, type="bca"?)
> # # # # # # # # # # # # # # # # # # # # # # # # #
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.